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Occam learning
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the Occam framework can be used to produce tighter bounds on the sample complexity of classical problems including conjunctions, conjunctions with fewBretagnolle–Huber inequality (1,489 words) [view diff] no match in snippet view article find links to article
In information theory, the Bretagnolle–Huber inequality bounds the total variation distance between two probability distributions P{\displaystyle P} andDistribution learning theory (3,845 words) [view diff] exact match in snippet view article find links to article
distributions C {\displaystyle \textstyle C} . This quantity is called sample complexity of the learning algorithm. In order for the problem of distributionVapnik–Chervonenkis dimension (2,733 words) [view diff] no match in snippet view article find links to article
training-error. This is due to overfitting). The VC dimension also appears in sample-complexity bounds. A space of binary functions with VC dimension D{\displaystyleThompson sampling (1,612 words) [view diff] case mismatch in snippet view article find links to article
Daniel J. Russo and Benjamin Van Roy (2013), "Eluder Dimension and the Sample Complexity of Optimistic Exploration", Advances in Neural Information ProcessingShai Ben-David (581 words) [view diff] exact match in snippet view article find links to article
2014). He received the best paper award at NeurIPS 2018. for work on sample complexity of distribution learning problems. He was the President of the AssociationScenario optimization (1,247 words) [view diff] exact match in snippet view article find links to article
approach, named "Repetitive Scenario Design" aims at reducing the sample complexity of the solution by repeatedly alternating a scenario design phaseThomas G. Dietterich (2,777 words) [view diff] case mismatch in snippet view article find links to article
Siddiqui, Alan Fern, Thomas G. Dietterich, Shubhomoy Das (2016). Finite Sample Complexity of Rare Pattern Anomaly Detection. Uncertainty in Artificial IntelligenceQuantum machine learning (10,189 words) [view diff] exact match in snippet view article find links to article
of examples needed: for every concept class, classical and quantum sample complexity are the same up to constant factors. However, for learning under someEnsemble learning (6,081 words) [view diff] exact match in snippet view article find links to article
David; Kearns, Michael; Schapire, Robert E. (1994). "Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension"Chemical biology (6,090 words) [view diff] exact match in snippet view article find links to article
barrier for their detection. Chemical biology methods can reduce sample complexity by selective enrichment using affinity chromatography. This involvesVlad Voroninski (618 words) [view diff] exact match in snippet view article find links to article
connected the fields of deep learning and inverse problems, resolving the sample complexity bottleneck for compressive phase retrieval. Voroninski was awardedProteomics (8,907 words) [view diff] exact match in snippet view article find links to article
below). For the analysis of complex biological samples, a reduction of sample complexity is required. This may be performed off-line by one-dimensional orLarge width limits of neural networks (869 words) [view diff] exact match in snippet view article find links to article
Cite journal requires |journal= (help) Bartlett, P.L. (1998). "The sample complexity of pattern classification with neural networks: the size of the weightsGrigory Yaroslavtsev (858 words) [view diff] case mismatch in snippet view article find links to article
Artificial Intelligence). Retrieved 8 August 2023. "Tree Learning: Optimal Sample Complexity and Algorithms" (PDF). AAAI 2023 (37th AAAI Conference on ArtificialMatrix completion (5,581 words) [view diff] exact match in snippet view article find links to article
1 / ϵ ) ) {\displaystyle O(\log(1/\epsilon ))} steps. In terms of sample complexity ( | Ω | {\displaystyle |\Omega |} ), theoretically, Alternating MinimizationSparse Fourier transform (1,536 words) [view diff] exact match in snippet view article find links to article
"Sparse fourier transform in any constant dimension with nearly-optimal sample complexity in sublinear time". Proceedings of the forty-eighth annual ACM symposiumLoss functions for classification (3,989 words) [view diff] exact match in snippet view article find links to article
excessively, leading to slower convergence rates (with regards to sample complexity) than for the logistic loss or hinge loss functions. In addition,Boson sampling (6,965 words) [view diff] exact match in snippet view article find links to article
; Aolita, L.; Eisert, J. (2013). "Boson-Sampling in the light of sample complexity". arXiv:1306.3995 [quant-ph]. Aaronson, Scott; Arkhipov, Alex (2013)Reinforcement learning from human feedback (4,611 words) [view diff] exact match in snippet view article find links to article
its policy immediately, have been mathematically studied proving sample complexity bounds for RLHF under different feedback models. In the offline data